Overview

Dataset statistics

Number of variables16
Number of observations50
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.4 KiB
Average record size in memory130.6 B

Variable types

Text3
Numeric11
Categorical2

Alerts

acousticness is highly overall correlated with loudnessHigh correlation
danceability is highly overall correlated with valenceHigh correlation
energy is highly overall correlated with loudnessHigh correlation
loudness is highly overall correlated with acousticness and 1 other fieldsHigh correlation
valence is highly overall correlated with danceabilityHigh correlation
time_signature is highly imbalanced (59.6%)Imbalance
Track_ID has unique valuesUnique
Track_Name has unique valuesUnique
tempo has unique valuesUnique
duration_ms has unique valuesUnique
key has 1 (2.0%) zerosZeros
instrumentalness has 22 (44.0%) zerosZeros

Reproduction

Analysis started2023-11-24 08:10:54.338846
Analysis finished2023-11-24 08:11:17.607557
Duration23.27 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

Track_ID
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-11-24T11:11:17.876210image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters1100
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st row1eldTykrnkEBLX41bk5eMw
2nd row2PWJx5FwMrMVEaTjwYait1
3rd row2HafqoJbgXdtjwCOvNEF14
4th row2ImdwbujxKFxN1UxEvf2dD
5th row30D9x5LFgL2o9xidjX2wtE
ValueCountFrequency (%)
1eldtykrnkeblx41bk5emw 1
 
2.0%
31nfdeooleq7dn3umcieb5 1
 
2.0%
1odexi7rdwc4bt515ltawj 1
 
2.0%
2hafqojbgxdtjwcovnef14 1
 
2.0%
2imdwbujxkfxn1uxevf2dd 1
 
2.0%
30d9x5lfgl2o9xidjx2wte 1
 
2.0%
3rugc1vupkdg9czfhmur1t 1
 
2.0%
2bvjmy4mp5q1ahl0laetd6 1
 
2.0%
5mjyqaktjmjcmkcuicqz4s 1
 
2.0%
0eznyxyu7ghzj2tn8qythj 1
 
2.0%
Other values (40) 40
80.0%
2023-11-24T11:11:18.447745image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 36
 
3.3%
2 31
 
2.8%
v 30
 
2.7%
5 26
 
2.4%
3 26
 
2.4%
t 26
 
2.4%
k 25
 
2.3%
j 25
 
2.3%
I 23
 
2.1%
M 23
 
2.1%
Other values (52) 829
75.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 455
41.4%
Uppercase Letter 424
38.5%
Decimal Number 221
20.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
v 30
 
6.6%
t 26
 
5.7%
k 25
 
5.5%
j 25
 
5.5%
d 22
 
4.8%
y 22
 
4.8%
o 20
 
4.4%
e 19
 
4.2%
x 19
 
4.2%
r 19
 
4.2%
Other values (16) 228
50.1%
Uppercase Letter
ValueCountFrequency (%)
I 23
 
5.4%
M 23
 
5.4%
D 22
 
5.2%
R 20
 
4.7%
F 20
 
4.7%
U 18
 
4.2%
K 18
 
4.2%
V 18
 
4.2%
N 18
 
4.2%
J 18
 
4.2%
Other values (16) 226
53.3%
Decimal Number
ValueCountFrequency (%)
1 36
16.3%
2 31
14.0%
5 26
11.8%
3 26
11.8%
6 21
9.5%
4 21
9.5%
7 19
8.6%
0 17
7.7%
9 14
 
6.3%
8 10
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 879
79.9%
Common 221
 
20.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
v 30
 
3.4%
t 26
 
3.0%
k 25
 
2.8%
j 25
 
2.8%
I 23
 
2.6%
M 23
 
2.6%
d 22
 
2.5%
y 22
 
2.5%
D 22
 
2.5%
o 20
 
2.3%
Other values (42) 641
72.9%
Common
ValueCountFrequency (%)
1 36
16.3%
2 31
14.0%
5 26
11.8%
3 26
11.8%
6 21
9.5%
4 21
9.5%
7 19
8.6%
0 17
7.7%
9 14
 
6.3%
8 10
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 36
 
3.3%
2 31
 
2.8%
v 30
 
2.7%
5 26
 
2.4%
3 26
 
2.4%
t 26
 
2.4%
k 25
 
2.3%
j 25
 
2.3%
I 23
 
2.1%
M 23
 
2.1%
Other values (52) 829
75.4%

Track_Name
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-11-24T11:11:18.901199image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length39
Median length21.5
Mean length11.74
Min length3

Characters and Unicode

Total characters587
Distinct characters60
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st rowPetit génie
2nd rowFLASHBACK
3rd rowSi No Estás
4th rowLAISSE MOI
5th rowCasanova
ValueCountFrequency (%)
feat 5
 
4.6%
the 4
 
3.7%
love 3
 
2.8%
2
 
1.8%
la 2
 
1.8%
no 2
 
1.8%
chemin 2
 
1.8%
allemand 1
 
0.9%
techno 1
 
0.9%
ton 1
 
0.9%
Other values (86) 86
78.9%
2023-11-24T11:11:19.516809image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
59
 
10.1%
e 52
 
8.9%
a 46
 
7.8%
o 33
 
5.6%
i 33
 
5.6%
r 28
 
4.8%
t 26
 
4.4%
n 24
 
4.1%
l 17
 
2.9%
S 15
 
2.6%
Other values (50) 254
43.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 368
62.7%
Uppercase Letter 133
 
22.7%
Space Separator 59
 
10.1%
Other Punctuation 7
 
1.2%
Close Punctuation 6
 
1.0%
Open Punctuation 6
 
1.0%
Decimal Number 5
 
0.9%
Dash Punctuation 3
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 52
14.1%
a 46
12.5%
o 33
 
9.0%
i 33
 
9.0%
r 28
 
7.6%
t 26
 
7.1%
n 24
 
6.5%
l 17
 
4.6%
s 13
 
3.5%
u 12
 
3.3%
Other values (16) 84
22.8%
Uppercase Letter
ValueCountFrequency (%)
S 15
 
11.3%
A 14
 
10.5%
L 11
 
8.3%
T 9
 
6.8%
D 9
 
6.8%
C 8
 
6.0%
M 8
 
6.0%
E 8
 
6.0%
R 7
 
5.3%
N 6
 
4.5%
Other values (13) 38
28.6%
Decimal Number
ValueCountFrequency (%)
7 1
20.0%
1 1
20.0%
2 1
20.0%
3 1
20.0%
4 1
20.0%
Other Punctuation
ValueCountFrequency (%)
. 5
71.4%
' 2
 
28.6%
Space Separator
ValueCountFrequency (%)
59
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 501
85.3%
Common 86
 
14.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 52
 
10.4%
a 46
 
9.2%
o 33
 
6.6%
i 33
 
6.6%
r 28
 
5.6%
t 26
 
5.2%
n 24
 
4.8%
l 17
 
3.4%
S 15
 
3.0%
A 14
 
2.8%
Other values (39) 213
42.5%
Common
ValueCountFrequency (%)
59
68.6%
) 6
 
7.0%
( 6
 
7.0%
. 5
 
5.8%
- 3
 
3.5%
' 2
 
2.3%
7 1
 
1.2%
1 1
 
1.2%
2 1
 
1.2%
3 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 584
99.5%
None 3
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
59
 
10.1%
e 52
 
8.9%
a 46
 
7.9%
o 33
 
5.7%
i 33
 
5.7%
r 28
 
4.8%
t 26
 
4.5%
n 24
 
4.1%
l 17
 
2.9%
S 15
 
2.6%
Other values (48) 251
43.0%
None
ValueCountFrequency (%)
é 2
66.7%
á 1
33.3%
Distinct45
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-11-24T11:11:19.855185image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length69
Median length29.5
Mean length17.44
Min length7

Characters and Unicode

Total characters872
Distinct characters62
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)84.0%

Sample

1st row['Jungeli', 'Imen Es', 'Alonzo', 'Abou Debeing', 'Lossa']
2nd row['Favé', 'Gazo']
3rd row['iñigo quintero']
4th row['KeBlack']
5th row['Soolking', 'Gazo']
ValueCountFrequency (%)
gazo 6
 
5.8%
werenoi 4
 
3.8%
tiakola 3
 
2.9%
macklemore 2
 
1.9%
dua 2
 
1.9%
favé 2
 
1.9%
sdm 2
 
1.9%
ryan 2
 
1.9%
lipa 2
 
1.9%
lewis 2
 
1.9%
Other values (76) 77
74.0%
2023-11-24T11:11:20.459896image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 153
17.5%
a 59
 
6.8%
54
 
6.2%
[ 50
 
5.7%
] 50
 
5.7%
e 46
 
5.3%
o 38
 
4.4%
i 38
 
4.4%
n 29
 
3.3%
, 27
 
3.1%
Other values (52) 328
37.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 395
45.3%
Other Punctuation 183
21.0%
Uppercase Letter 137
 
15.7%
Space Separator 54
 
6.2%
Open Punctuation 50
 
5.7%
Close Punctuation 50
 
5.7%
Dash Punctuation 2
 
0.2%
Decimal Number 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 59
14.9%
e 46
11.6%
o 38
 
9.6%
i 38
 
9.6%
n 29
 
7.3%
r 24
 
6.1%
s 18
 
4.6%
l 17
 
4.3%
m 14
 
3.5%
c 13
 
3.3%
Other values (19) 99
25.1%
Uppercase Letter
ValueCountFrequency (%)
S 12
 
8.8%
T 12
 
8.8%
D 11
 
8.0%
G 11
 
8.0%
L 11
 
8.0%
M 9
 
6.6%
K 8
 
5.8%
O 7
 
5.1%
N 7
 
5.1%
R 7
 
5.1%
Other values (14) 42
30.7%
Other Punctuation
ValueCountFrequency (%)
' 153
83.6%
, 27
 
14.8%
" 2
 
1.1%
& 1
 
0.5%
Space Separator
ValueCountFrequency (%)
54
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 50
100.0%
Close Punctuation
ValueCountFrequency (%)
] 50
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 532
61.0%
Common 340
39.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 59
 
11.1%
e 46
 
8.6%
o 38
 
7.1%
i 38
 
7.1%
n 29
 
5.5%
r 24
 
4.5%
s 18
 
3.4%
l 17
 
3.2%
m 14
 
2.6%
c 13
 
2.4%
Other values (43) 236
44.4%
Common
ValueCountFrequency (%)
' 153
45.0%
54
 
15.9%
[ 50
 
14.7%
] 50
 
14.7%
, 27
 
7.9%
" 2
 
0.6%
- 2
 
0.6%
2 1
 
0.3%
& 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 867
99.4%
None 5
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 153
17.6%
a 59
 
6.8%
54
 
6.2%
[ 50
 
5.8%
] 50
 
5.8%
e 46
 
5.3%
o 38
 
4.4%
i 38
 
4.4%
n 29
 
3.3%
, 27
 
3.1%
Other values (49) 323
37.3%
None
ValueCountFrequency (%)
é 3
60.0%
ö 1
 
20.0%
ñ 1
 
20.0%

danceability
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6839
Minimum0.284
Maximum0.931
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:11:20.726729image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.284
5-th percentile0.47335
Q10.625
median0.703
Q30.76425
95-th percentile0.8783
Maximum0.931
Range0.647
Interquartile range (IQR)0.13925

Descriptive statistics

Standard deviation0.12982928
Coefficient of variation (CV)0.18983665
Kurtosis1.2133172
Mean0.6839
Median Absolute Deviation (MAD)0.071
Skewness-0.7942227
Sum34.195
Variance0.016855643
MonotonicityNot monotonic
2023-11-24T11:11:20.977192image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0.765 2
 
4.0%
0.801 2
 
4.0%
0.673 2
 
4.0%
0.794 1
 
2.0%
0.561 1
 
2.0%
0.89 1
 
2.0%
0.596 1
 
2.0%
0.755 1
 
2.0%
0.696 1
 
2.0%
0.727 1
 
2.0%
Other values (37) 37
74.0%
ValueCountFrequency (%)
0.284 1
2.0%
0.355 1
2.0%
0.445 1
2.0%
0.508 1
2.0%
0.524 1
2.0%
0.536 1
2.0%
0.537 1
2.0%
0.545 1
2.0%
0.561 1
2.0%
0.587 1
2.0%
ValueCountFrequency (%)
0.931 1
2.0%
0.897 1
2.0%
0.89 1
2.0%
0.864 1
2.0%
0.825 1
2.0%
0.819 1
2.0%
0.801 2
4.0%
0.798 1
2.0%
0.794 1
2.0%
0.775 1
2.0%

energy
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.67428
Minimum0.29
Maximum0.927
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:11:21.231040image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.29
5-th percentile0.4327
Q10.59575
median0.675
Q30.7565
95-th percentile0.85535
Maximum0.927
Range0.637
Interquartile range (IQR)0.16075

Descriptive statistics

Standard deviation0.12978051
Coefficient of variation (CV)0.19247273
Kurtosis0.67256126
Mean0.67428
Median Absolute Deviation (MAD)0.082
Skewness-0.48824127
Sum33.714
Variance0.016842981
MonotonicityNot monotonic
2023-11-24T11:11:21.479543image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0.717 3
 
6.0%
0.675 2
 
4.0%
0.593 2
 
4.0%
0.604 2
 
4.0%
0.636 1
 
2.0%
0.793 1
 
2.0%
0.725 1
 
2.0%
0.863 1
 
2.0%
0.555 1
 
2.0%
0.722 1
 
2.0%
Other values (35) 35
70.0%
ValueCountFrequency (%)
0.29 1
2.0%
0.421 1
2.0%
0.43 1
2.0%
0.436 1
2.0%
0.523 1
2.0%
0.537 1
2.0%
0.555 1
2.0%
0.556 1
2.0%
0.564 1
2.0%
0.578 1
2.0%
ValueCountFrequency (%)
0.927 1
2.0%
0.924 1
2.0%
0.863 1
2.0%
0.846 1
2.0%
0.845 1
2.0%
0.832 1
2.0%
0.824 1
2.0%
0.808 1
2.0%
0.793 1
2.0%
0.789 1
2.0%

key
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.76
Minimum0
Maximum11
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:11:21.700729image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5.5
Q39.75
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)7.75

Descriptive statistics

Standard deviation3.7501565
Coefficient of variation (CV)0.65106883
Kurtosis-1.526969
Mean5.76
Median Absolute Deviation (MAD)3.5
Skewness0.11320633
Sum288
Variance14.063673
MonotonicityNot monotonic
2023-11-24T11:11:21.902508image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11 8
16.0%
1 7
14.0%
2 6
12.0%
10 5
10.0%
3 5
10.0%
4 4
8.0%
6 3
 
6.0%
7 3
 
6.0%
9 3
 
6.0%
8 3
 
6.0%
Other values (2) 3
 
6.0%
ValueCountFrequency (%)
0 1
 
2.0%
1 7
14.0%
2 6
12.0%
3 5
10.0%
4 4
8.0%
5 2
 
4.0%
6 3
6.0%
7 3
6.0%
8 3
6.0%
9 3
6.0%
ValueCountFrequency (%)
11 8
16.0%
10 5
10.0%
9 3
 
6.0%
8 3
 
6.0%
7 3
 
6.0%
6 3
 
6.0%
5 2
 
4.0%
4 4
8.0%
3 5
10.0%
2 6
12.0%

loudness
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.24744
Minimum-9.475
Maximum-3.145
Zeros0
Zeros (%)0.0%
Negative50
Negative (%)100.0%
Memory size532.0 B
2023-11-24T11:11:22.129758image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-9.475
5-th percentile-8.59745
Q1-7.73475
median-6.3665
Q3-4.743
95-th percentile-3.43945
Maximum-3.145
Range6.33
Interquartile range (IQR)2.99175

Descriptive statistics

Standard deviation1.7429652
Coefficient of variation (CV)-0.2789887
Kurtosis-1.1877609
Mean-6.24744
Median Absolute Deviation (MAD)1.509
Skewness0.12006331
Sum-312.372
Variance3.0379276
MonotonicityNot monotonic
2023-11-24T11:11:22.388781image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
-5.932 2
 
4.0%
-4.93 1
 
2.0%
-4.818 1
 
2.0%
-4.666 1
 
2.0%
-5.213 1
 
2.0%
-4.929 1
 
2.0%
-7.752 1
 
2.0%
-3.495 1
 
2.0%
-6.72 1
 
2.0%
-5.84 1
 
2.0%
Other values (39) 39
78.0%
ValueCountFrequency (%)
-9.475 1
2.0%
-8.72 1
2.0%
-8.651 1
2.0%
-8.532 1
2.0%
-8.461 1
2.0%
-8.438 1
2.0%
-8.307 1
2.0%
-8.251 1
2.0%
-8.182 1
2.0%
-7.932 1
2.0%
ValueCountFrequency (%)
-3.145 1
2.0%
-3.18 1
2.0%
-3.394 1
2.0%
-3.495 1
2.0%
-3.546 1
2.0%
-4.175 1
2.0%
-4.265 1
2.0%
-4.409 1
2.0%
-4.468 1
2.0%
-4.568 1
2.0%

mode
Categorical

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0
38 
1
12 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 38
76.0%
1 12
 
24.0%

Length

2023-11-24T11:11:22.631719image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T11:11:22.806844image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 38
76.0%
1 12
 
24.0%

Most occurring characters

ValueCountFrequency (%)
0 38
76.0%
1 12
 
24.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38
76.0%
1 12
 
24.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38
76.0%
1 12
 
24.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38
76.0%
1 12
 
24.0%

speechiness
Real number (ℝ)

Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.123854
Minimum0.0277
Maximum0.811
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:11:23.011387image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.0277
5-th percentile0.030855
Q10.042325
median0.07365
Q30.16
95-th percentile0.32695
Maximum0.811
Range0.7833
Interquartile range (IQR)0.117675

Descriptive statistics

Standard deviation0.13483357
Coefficient of variation (CV)1.0886493
Kurtosis13.137322
Mean0.123854
Median Absolute Deviation (MAD)0.03925
Skewness3.0902983
Sum6.1927
Variance0.018180093
MonotonicityNot monotonic
2023-11-24T11:11:23.290962image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.16 2
 
4.0%
0.25 1
 
2.0%
0.196 1
 
2.0%
0.0722 1
 
2.0%
0.133 1
 
2.0%
0.0469 1
 
2.0%
0.0414 1
 
2.0%
0.233 1
 
2.0%
0.0755 1
 
2.0%
0.152 1
 
2.0%
Other values (39) 39
78.0%
ValueCountFrequency (%)
0.0277 1
2.0%
0.0285 1
2.0%
0.03 1
2.0%
0.0319 1
2.0%
0.0335 1
2.0%
0.0337 1
2.0%
0.0338 1
2.0%
0.035 1
2.0%
0.0353 1
2.0%
0.0375 1
2.0%
ValueCountFrequency (%)
0.811 1
2.0%
0.391 1
2.0%
0.349 1
2.0%
0.3 1
2.0%
0.28 1
2.0%
0.25 1
2.0%
0.233 1
2.0%
0.22 1
2.0%
0.21 1
2.0%
0.208 1
2.0%

acousticness
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2818342
Minimum0.001
Maximum0.881
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:11:23.560885image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.0034625
Q10.071875
median0.2445
Q30.38175
95-th percentile0.82865
Maximum0.881
Range0.88
Interquartile range (IQR)0.309875

Descriptive statistics

Standard deviation0.25088934
Coefficient of variation (CV)0.89020189
Kurtosis0.22279057
Mean0.2818342
Median Absolute Deviation (MAD)0.16805
Skewness0.98057524
Sum14.09171
Variance0.06294546
MonotonicityNot monotonic
2023-11-24T11:11:23.814940image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.256 2
 
4.0%
0.103 1
 
2.0%
0.151 1
 
2.0%
0.0378 1
 
2.0%
0.00989 1
 
2.0%
0.246 1
 
2.0%
0.0673 1
 
2.0%
0.0856 1
 
2.0%
0.354 1
 
2.0%
0.221 1
 
2.0%
Other values (39) 39
78.0%
ValueCountFrequency (%)
0.001 1
2.0%
0.00257 1
2.0%
0.00335 1
2.0%
0.0036 1
2.0%
0.00989 1
2.0%
0.011 1
2.0%
0.0207 1
2.0%
0.0267 1
2.0%
0.0297 1
2.0%
0.0311 1
2.0%
ValueCountFrequency (%)
0.881 1
2.0%
0.873 1
2.0%
0.83 1
2.0%
0.827 1
2.0%
0.701 1
2.0%
0.695 1
2.0%
0.677 1
2.0%
0.508 1
2.0%
0.463 1
2.0%
0.457 1
2.0%

instrumentalness
Real number (ℝ)

ZEROS 

Distinct28
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0022205184
Minimum0
Maximum0.0711
Zeros22
Zeros (%)44.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:11:24.045992image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.92 × 10-6
Q30.000441
95-th percentile0.003762
Maximum0.0711
Range0.0711
Interquartile range (IQR)0.000441

Descriptive statistics

Standard deviation0.010260074
Coefficient of variation (CV)4.620576
Kurtosis43.766806
Mean0.0022205184
Median Absolute Deviation (MAD)2.92 × 10-6
Skewness6.4890449
Sum0.11102592
Variance0.00010526912
MonotonicityNot monotonic
2023-11-24T11:11:24.280952image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 22
44.0%
0.000441 2
 
4.0%
1.24 × 10-51
 
2.0%
0.00013 1
 
2.0%
0.00138 1
 
2.0%
0.000498 1
 
2.0%
1.9 × 10-51
 
2.0%
0.00258 1
 
2.0%
1.99 × 10-61
 
2.0%
1.65 × 10-51
 
2.0%
Other values (18) 18
36.0%
ValueCountFrequency (%)
0 22
44.0%
1.66 × 10-61
 
2.0%
1.79 × 10-61
 
2.0%
1.99 × 10-61
 
2.0%
3.85 × 10-61
 
2.0%
4.63 × 10-61
 
2.0%
1.01 × 10-51
 
2.0%
1.24 × 10-51
 
2.0%
1.65 × 10-51
 
2.0%
1.9 × 10-51
 
2.0%
ValueCountFrequency (%)
0.0711 1
2.0%
0.0171 1
2.0%
0.00378 1
2.0%
0.00374 1
2.0%
0.00274 1
2.0%
0.00258 1
2.0%
0.00197 1
2.0%
0.00195 1
2.0%
0.00153 1
2.0%
0.00144 1
2.0%

liveness
Real number (ℝ)

Distinct45
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.131494
Minimum0.0566
Maximum0.363
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:11:24.520171image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.0566
5-th percentile0.066705
Q10.095925
median0.113
Q30.137
95-th percentile0.2663
Maximum0.363
Range0.3064
Interquartile range (IQR)0.041075

Descriptive statistics

Standard deviation0.064240563
Coefficient of variation (CV)0.48854368
Kurtosis4.2856838
Mean0.131494
Median Absolute Deviation (MAD)0.0181
Skewness2.0233472
Sum6.5747
Variance0.00412685
MonotonicityNot monotonic
2023-11-24T11:11:24.773471image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0.113 3
 
6.0%
0.137 2
 
4.0%
0.123 2
 
4.0%
0.106 2
 
4.0%
0.0748 1
 
2.0%
0.0954 1
 
2.0%
0.0683 1
 
2.0%
0.296 1
 
2.0%
0.159 1
 
2.0%
0.0896 1
 
2.0%
Other values (35) 35
70.0%
ValueCountFrequency (%)
0.0566 1
2.0%
0.058 1
2.0%
0.0654 1
2.0%
0.0683 1
2.0%
0.0748 1
2.0%
0.0771 1
2.0%
0.0896 1
2.0%
0.0924 1
2.0%
0.093 1
2.0%
0.0944 1
2.0%
ValueCountFrequency (%)
0.363 1
2.0%
0.329 1
2.0%
0.296 1
2.0%
0.23 1
2.0%
0.219 1
2.0%
0.217 1
2.0%
0.207 1
2.0%
0.196 1
2.0%
0.167 1
2.0%
0.159 1
2.0%

valence
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.560918
Minimum0.0859
Maximum0.967
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:11:25.036401image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.0859
5-th percentile0.21835
Q10.425
median0.5535
Q30.7395
95-th percentile0.8615
Maximum0.967
Range0.8811
Interquartile range (IQR)0.3145

Descriptive statistics

Standard deviation0.2056915
Coefficient of variation (CV)0.36670512
Kurtosis-0.42170509
Mean0.560918
Median Absolute Deviation (MAD)0.1515
Skewness-0.20493815
Sum28.0459
Variance0.042308993
MonotonicityNot monotonic
2023-11-24T11:11:25.288390image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.601 2
 
4.0%
0.434 2
 
4.0%
0.775 1
 
2.0%
0.856 1
 
2.0%
0.762 1
 
2.0%
0.66 1
 
2.0%
0.569 1
 
2.0%
0.519 1
 
2.0%
0.392 1
 
2.0%
0.83 1
 
2.0%
Other values (38) 38
76.0%
ValueCountFrequency (%)
0.0859 1
2.0%
0.131 1
2.0%
0.199 1
2.0%
0.242 1
2.0%
0.279 1
2.0%
0.303 1
2.0%
0.324 1
2.0%
0.363 1
2.0%
0.371 1
2.0%
0.381 1
2.0%
ValueCountFrequency (%)
0.967 1
2.0%
0.88 1
2.0%
0.866 1
2.0%
0.856 1
2.0%
0.846 1
2.0%
0.844 1
2.0%
0.83 1
2.0%
0.775 1
2.0%
0.762 1
2.0%
0.758 1
2.0%

tempo
Real number (ℝ)

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.52714
Minimum72.494
Maximum196.007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:11:25.536942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum72.494
5-th percentile79.8095
Q1105.58525
median125.0185
Q3139.523
95-th percentile165.9546
Maximum196.007
Range123.513
Interquartile range (IQR)33.93775

Descriptive statistics

Standard deviation26.836837
Coefficient of variation (CV)0.21725458
Kurtosis0.039347773
Mean123.52714
Median Absolute Deviation (MAD)15.9365
Skewness0.16171266
Sum6176.357
Variance720.21584
MonotonicityNot monotonic
2023-11-24T11:11:25.832911image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.032 1
 
2.0%
138.065 1
 
2.0%
101.005 1
 
2.0%
117.999 1
 
2.0%
122.004 1
 
2.0%
150.134 1
 
2.0%
117.119 1
 
2.0%
117.187 1
 
2.0%
91.452 1
 
2.0%
74.977 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
72.494 1
2.0%
74.977 1
2.0%
79.301 1
2.0%
80.431 1
2.0%
82.024 1
2.0%
88.038 1
2.0%
91.452 1
2.0%
92.475 1
2.0%
93.384 1
2.0%
98.224 1
2.0%
ValueCountFrequency (%)
196.007 1
2.0%
169.982 1
2.0%
168.345 1
2.0%
163.033 1
2.0%
162.106 1
2.0%
159.92 1
2.0%
150.134 1
2.0%
150.002 1
2.0%
146.097 1
2.0%
141.938 1
2.0%

duration_ms
Real number (ℝ)

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189715.5
Minimum131872
Maximum258432
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:11:26.103899image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum131872
5-th percentile144694.2
Q1171649.5
median183194
Q3211803
95-th percentile243064
Maximum258432
Range126560
Interquartile range (IQR)40153.5

Descriptive statistics

Standard deviation31644.858
Coefficient of variation (CV)0.16680165
Kurtosis-0.43394535
Mean189715.5
Median Absolute Deviation (MAD)18340
Skewness0.40375097
Sum9485775
Variance1.001397 × 109
MonotonicityNot monotonic
2023-11-24T11:11:26.387599image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
217260 1
 
2.0%
225137 1
 
2.0%
188485 1
 
2.0%
166529 1
 
2.0%
171211 1
 
2.0%
156413 1
 
2.0%
189618 1
 
2.0%
200256 1
 
2.0%
212093 1
 
2.0%
175947 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
131872 1
2.0%
132359 1
2.0%
143265 1
2.0%
146441 1
2.0%
147788 1
2.0%
152547 1
2.0%
155905 1
2.0%
156413 1
2.0%
162840 1
2.0%
165507 1
2.0%
ValueCountFrequency (%)
258432 1
2.0%
255160 1
2.0%
244360 1
2.0%
241480 1
2.0%
240413 1
2.0%
238653 1
2.0%
237160 1
2.0%
230480 1
2.0%
225137 1
2.0%
217260 1
2.0%

time_signature
Categorical

IMBALANCE 

Distinct3
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
4
44 
3
 
4
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 44
88.0%
3 4
 
8.0%
1 2
 
4.0%

Length

2023-11-24T11:11:26.652607image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T11:11:26.836114image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
4 44
88.0%
3 4
 
8.0%
1 2
 
4.0%

Most occurring characters

ValueCountFrequency (%)
4 44
88.0%
3 4
 
8.0%
1 2
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 44
88.0%
3 4
 
8.0%
1 2
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 44
88.0%
3 4
 
8.0%
1 2
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 44
88.0%
3 4
 
8.0%
1 2
 
4.0%

Interactions

2023-11-24T11:11:15.271336image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:10:54.889483image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:10:56.984351image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:10:58.919321image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:00.852971image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:03.398509image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:05.577029image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:07.928822image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:10.042323image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:11.881019image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:13.538119image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:15.430444image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:10:55.124597image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:10:57.146332image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:10:59.095608image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:01.073573image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:03.644687image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:05.744308image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:08.101858image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:10.212142image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:12.032284image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:13.701845image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:15.593391image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:10:55.302320image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:10:57.302365image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:10:59.256973image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:01.313818image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:03.836251image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:05.909683image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:08.284274image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:10.381115image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:12.187467image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:13.859608image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:15.744015image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:10:55.600823image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:10:57.453133image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:10:59.419256image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:01.512581image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:03.994427image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:06.122134image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:08.456552image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:10.534595image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:12.329664image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:14.010336image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:15.903646image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:10:55.781688image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:10:57.612922image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:10:59.665809image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:01.711156image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:04.225633image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:06.354812image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:08.643152image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:10.701431image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:12.478629image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:14.170233image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:16.079125image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:10:55.960620image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:10:57.811854image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:10:59.864994image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:02.098963image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:04.467843image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:06.630816image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:08.922708image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:10.923956image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:12.642855image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:14.342501image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:16.225635image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:10:56.113506image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:10:57.984003image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:00.016247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:02.259025image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:04.659575image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:06.879291image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:09.148173image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:11.084136image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:12.780116image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:14.487024image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:16.393065image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:10:56.284485image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:10:58.166218image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:00.213088image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:02.511956image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:04.850575image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:07.159872image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:09.334170image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:11.255766image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:12.941533image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:14.651857image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:16.551347image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:10:56.474505image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:10:58.352901image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:00.380325image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:02.709048image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:05.050543image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:07.349924image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:09.526124image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:11.416965image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:13.096736image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:14.809443image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:16.701251image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:10:56.649191image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:10:58.511330image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:00.535492image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:02.982781image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:05.218365image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:07.501661image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:09.700126image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:11.564132image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:13.236049image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:14.956530image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:16.863984image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:10:56.817960image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:10:58.714416image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:00.688395image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:03.211978image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:05.380937image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:07.708230image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:09.867483image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:11.721312image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:13.385705image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:11:15.111808image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-24T11:11:26.979173image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
acousticnessdanceabilityduration_msenergyinstrumentalnesskeylivenessloudnessmodespeechinesstempotime_signaturevalence
acousticness1.0000.0170.158-0.496-0.0830.0860.147-0.5170.0180.059-0.0420.079-0.184
danceability0.0171.000-0.2160.0310.073-0.0740.2440.1150.0000.367-0.2910.1740.616
duration_ms0.158-0.2161.000-0.209-0.063-0.1190.078-0.0830.0000.023-0.1700.133-0.238
energy-0.4960.031-0.2091.0000.059-0.002-0.0880.6720.000-0.129-0.0220.0000.308
instrumentalness-0.0830.073-0.0630.0591.000-0.0290.0870.0590.000-0.0710.1650.000-0.161
key0.086-0.074-0.119-0.002-0.0291.0000.249-0.1120.1130.1340.0150.000-0.240
liveness0.1470.2440.078-0.0880.0870.2491.000-0.0230.0000.107-0.0660.0000.034
loudness-0.5170.115-0.0830.6720.059-0.112-0.0231.0000.000-0.331-0.0640.0000.189
mode0.0180.0000.0000.0000.0000.1130.0000.0001.000-0.2580.1850.000-0.084
speechiness0.0590.3670.023-0.129-0.0710.1340.107-0.331-0.2581.000-0.0590.4660.236
tempo-0.042-0.291-0.170-0.0220.1650.015-0.066-0.0640.185-0.0591.0000.119-0.058
time_signature0.0790.1740.1330.0000.0000.0000.0000.0000.0000.4660.1191.0000.059
valence-0.1840.616-0.2380.308-0.161-0.2400.0340.189-0.0840.236-0.0580.0591.000

Missing values

2023-11-24T11:11:17.114914image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-24T11:11:17.471166image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Track_IDTrack_NameTrack_Artistsdanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mstime_signature
01eldTykrnkEBLX41bk5eMwPetit génie['Jungeli', 'Imen Es', 'Alonzo', 'Abou Debeing', 'Lossa']0.7940.5781-5.93200.25000.1030.0000120.36300.967126.0322172604
12PWJx5FwMrMVEaTjwYait1FLASHBACK['Favé', 'Gazo']0.7620.5646-6.93400.08800.3900.0000000.06540.744141.9381761204
22HafqoJbgXdtjwCOvNEF14Si No Estás['iñigo quintero']0.5370.4215-8.72010.02850.8270.0000000.13800.52498.2241840614
32ImdwbujxKFxN1UxEvf2dDLAISSE MOI['KeBlack']0.7650.71710-7.87700.34900.4560.0000100.12600.695120.0251686204
430D9x5LFgL2o9xidjX2wtECasanova['Soolking', 'Gazo']0.8010.8327-4.57900.20800.6770.0000290.11700.758132.0441890914
53rUGC1vUpkDG9CZFHMur1tgreedy['Tate McRae']0.7500.7336-3.18000.03190.2560.0000000.11400.844111.0181318721
62BvjmY4Mp5q1AHL0laetd6Saiyan["Heuss L'enfoiré", 'Gazo']0.7750.8087-4.56800.03530.3530.0000420.20700.638125.0131898404
75mjYQaktjmjcMKcUIcqz4sStrangers['Kenya Grace']0.6280.52311-8.30700.09460.7010.0027400.21900.416169.9821729654
80EzNyXyU7gHzj2TN8qYThjBolide allemand['SDM']0.7180.6434-7.47300.16000.4410.0000000.11300.645131.8251767474
924JrEVHDfohup0ypuOV7osLaboratoire['Werenoi']0.8010.6045-6.52500.21000.4630.0037800.11100.534125.0241628404
Track_IDTrack_NameTrack_Artistsdanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mstime_signature
407d391WfC0HtftRcetDFQUyNouvelles['PLK']0.5360.65511-7.63600.22000.01100.0004980.10600.434196.0071796534
413D29kjUyWxsT3jUUTtARVQDIE['Gazo']0.6950.6308-7.16000.03500.22900.0000000.11800.550130.9682404134
427A2DyRIqqelhZ4caPJMaFIUrus['Favé']0.7100.71211-7.43600.30000.02970.0013800.09750.596162.1061477884
43656vbT8JKkVx7g1yG12L89Avec Toi['OBOY']0.6610.66610-5.93200.02770.45700.0000000.13000.38192.4751938134
4422dUzMFttcR3uU17NcOAIvDesire (with Sam Smith)['Calvin Harris', 'Sam Smith']0.5870.92411-5.43300.04570.03110.0171000.23000.509140.0091791624
451HCbElP3lfeVIjHvANkroyCODE BARRE['Lacrim']0.5240.84611-7.87400.81100.33600.0000000.09240.51079.3012414801
467tChNCtleS9bUPD4uDXvJfLe feu['Kendji Girac', 'Vianney']0.6670.7461-4.17510.04510.26100.0000000.05660.54988.0382371604
4766TAAKeoOD0cFpVRug9IXZDemain['PLK']0.7260.6751-8.25110.16000.27200.0000000.12100.846137.9541830134
4807fbDnkKdZGk1gLvknxrnsRUNAWAY['OneRepublic']0.6240.6843-5.50700.05130.03360.0000000.07710.750163.0331432654
4922skzmqfdWrjJylampe0ktCan't Hold Us (feat. Ray Dalton)['Macklemore & Ryan Lewis', 'Macklemore', 'Ryan Lewis', 'Ray Dalton']0.6330.9272-4.46810.08390.02670.0000000.09860.880146.0972584324